{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T18:35:06Z","timestamp":1787855706640,"version":"build-2784847793"},"reference-count":43,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Endodontic J"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>Postoperative pain is a frequent clinical concern following endodontic treatment. This study aimed to develop and validate supervised machine learning models to predict the occurrence of postoperative pain in cases of irreversible pulpitis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>A prospective sample of 354 patients aged 18 to 60\u2009years undergoing standardised endodontic treatment was analysed. In the original randomised clinical trials from which the data were derived, each patient had only one eligible tooth included. Clinical variables included postoperative pain at 24 and 72\u2009h, treated tooth group, occlusal reduction, photobiomodulation therapy, use of non\u2010steroidal anti\u2010inflammatory drugs (NSAIDs), sex and age. Eight supervised machine learning algorithms were trained to predict pain occurrence, including Logistic Regression, Support Vector Machine, Gradient Boosting, Random Forest, Decision Tree, K\u2010Nearest Neighbours, AdaBoost and Multilayer Perceptron. The dataset was divided into training (70%) and testing (30%) sets using stratified sampling. Class imbalance in the training set, characterised by a lower proportion of cases with moderate or severe pain, was addressed using the Synthetic Minority Oversampling Technique. Hyperparameters were optimised through grid search combined with stratified five\u2010fold cross validation. Model performance was evaluated using the area under the curve (AUC), accuracy, precision, recall and F1\u2010score, with 95% confidence intervals estimated by bootstrapping.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The predictive models achieved good discrimination of pain outcomes. Logistic Regression showed the best test performance at 24\u2009h (AUC 0.74 [95% CI: 0.61 to 0.85], precision 0.81 [95% CI: 0.73 to 0.88]). At 72\u2009h, the Support Vector Machine achieved the highest performance (AUC 0.81 [95% CI: 0.69 to 0.92], precision 0.88 [95% CI: 0.79 to 0.94]). Age and sex emerged as the most influential predictors across models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Supervised machine learning models demonstrated promising performance for predicting postoperative pain following endodontic treatment. Logistic Regression and Support Vector Machine algorithms presented the most consistent results, supporting their potential clinical application for personalised pain management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/iej.70106","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T05:25:44Z","timestamp":1770009944000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Model for Predicting Postoperative Pain in Cases of Irreversible Pulpitis"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8952-0163","authenticated-orcid":false,"given":"Pedro Felipe de Jesus","family":"Freitas","sequence":"first","affiliation":[{"name":"School of Dentistry Tuiuti University of Paran\u00e1  Curitiba Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingrid Luiza Mendon\u00e7a","family":"Cunha","sequence":"additional","affiliation":[{"name":"School of Dentistry Federal University of Amazonas  Manaus Amazonas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2974-4743","authenticated-orcid":false,"given":"Thalita de Paris","family":"Matos","sequence":"additional","affiliation":[{"name":"School of Dentistry Tuiuti University of Paran\u00e1  Curitiba Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5649-7234","authenticated-orcid":false,"given":"Flares","family":"Baratto\u2010Filho","sequence":"additional","affiliation":[{"name":"Post\u2010Graduation Program in Health and Environment University of Joinville (Univille)  Joinville Santa Catarina Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7507-4667","authenticated-orcid":false,"given":"Bianca Marques de Mattos","family":"de Araujo","sequence":"additional","affiliation":[{"name":"School of Dentistry Tuiuti University of Paran\u00e1  Curitiba Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7696-7600","authenticated-orcid":false,"given":"Manoel Dami\u00e3o","family":"Sousa\u2010Neto","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry, Dental School of Ribeir\u00e3o Preto University of S\u00e3o Paulo  S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2469-0291","authenticated-orcid":false,"given":"Leandro de Moura","family":"Martins","sequence":"additional","affiliation":[{"name":"School of Dentistry Federal University of Amazonas  Manaus Amazonas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4439-0189","authenticated-orcid":false,"given":"Fernando Jos\u00e9","family":"Herkrath","sequence":"additional","affiliation":[{"name":"School of Dentistry Federal University of Amazonas  Manaus Amazonas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7677-7682","authenticated-orcid":false,"given":"Em\u00edlio Carlos","family":"Sponchiado\u2010J\u00fanior","sequence":"additional","affiliation":[{"name":"School of Dentistry Federal University of Amazonas  Manaus Amazonas Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1325-4248","authenticated-orcid":false,"given":"Cristiano Miranda","family":"de Araujo","sequence":"additional","affiliation":[{"name":"School of Dentistry Tuiuti University of Paran\u00e1  Curitiba Brazil"},{"name":"Post\u2010Graduation Program in Health and Environment University of Joinville (Univille)  Joinville Santa Catarina 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